AI networking platform enhances observability and AIOps

Juniper Networks has recently unveiled significant advancements in the Mist™ AI-native networking platform, introducing expanded insights for wired, wireless, and WAN customers and partners. The Enhanced Marvis Minis feature now extends digital experience twinning capabilities across the global WAN, encompassing both public and private cloud environments and applications. Additionally, a new Marvis Actions self-driving dashboard has been introduced to streamline network operations by automatically identifying and resolving network issues, continuously optimizing network performance without the need for manual intervention. Furthermore, an enhanced Marvis mobile client has been integrated to extend Mist’s industry-leading AI-native Operations (AIOps) to end-user devices.

According to Sudheer Matta, Senior Vice President of Products, Campus and Branch at Juniper Networks, “The Mist AI-native networking platform was purposefully designed to merge AI and networking to deliver exceptional operator and end-user experiences. These latest enhancements represent a shift from traditional observability to an AI-native model that truly comprehends user experience on a scalable level. The new Marvis Minis feature acts as a multitude of digital experience twins working together to proactively identify, learn, and take action before user experience is impacted. With Marvis Minis, Juniper is at the forefront of providing cutting-edge automation, insight, and assurance, paving the way for a fundamental transition to agentic AI within the networking industry.”

The Marvis Minis digital experience twinning capabilities have been enhanced to proactively analyze user experiences from end to end, identifying potential performance issues within applications. By offering new service level expectations (SLEs), Marvis Minis provides increased visibility into application performance at various levels, such as site-specific, across multiple sites, and regions within an ISP. This enhanced monitoring enables Marvis Minis to swiftly identify and resolve issues before they impact the end-user experience, offering a seamless experience powered by AI without the need for additional deployment on the customer side.

Designed for self-driving networks, the Marvis AI Assistant is equipped to proactively address network issues such as VLAN misconfigurations and network loops, optimize Radio Resource Management (RRM), and automate routine tasks like policy updates and firmware compliance, thereby enhancing overall operational efficiency. The new Marvis Actions dashboard view provides complete control over self-driving network operations, offering a detailed history of all proactive actions taken. This empowers customers to manage their network according to their preferences, with insights into how Marvis AI Assistant identifies and resolves each issue.

Moreover, the enhanced Marvis Client, an extension of the Marvis AI Assistant, leverages client-side telemetry from devices running on Android®, Windows®, and macOS® operating systems to provide deeper insights into user experiences. By transmitting rich data such as device type, operating system, radio hardware, firmware, and connectivity metrics in near real-time to the Mist cloud, Marvis AI Assistant generates actionable insights. When combined with data collected from Juniper Access Points, routers, switches, and firewalls, IT teams can proactively address performance issues, enhance troubleshooting, and ensure a consistently high-quality user experience, all without the need for additional software or hardware sensors.

Bob Laliberte, Principal Analyst at CUBE Research, emphasized the importance of adopting self-driving networks and agentic AI technologies to navigate the complexities of highly distributed networks. These advancements are poised to enhance operational efficiency, elevate customer experiences, and provide valuable business insights. By leveraging an innovative AI-native networking platform, Juniper Networks continues to set new standards for end-to-end visibility and proactive control in modern network environments, setting the stage for a more efficient and reliable networking landscape.